{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "# Ignore numpy dtype warnings. These warnings are caused by an interaction\n",
    "# between numpy and Cython and can be safely ignored.\n",
    "# Reference: https://stackoverflow.com/a/40846742\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.ufunc size changed\")\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import nbinteract as nbi\n",
    "\n",
    "sns.set()\n",
    "sns.set_context('talk')\n",
    "np.set_printoptions(threshold=20, precision=2, suppress=True)\n",
    "pd.options.display.max_rows = 7\n",
    "pd.options.display.max_columns = 8\n",
    "pd.set_option('precision', 2)\n",
    "# This option stops scientific notation for pandas\n",
    "# pd.set_option('display.float_format', '{:.2f}'.format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "def df_interact(df):\n",
    "    '''\n",
    "    Outputs sliders that show rows and columns of df\n",
    "    '''\n",
    "    def peek(row=0, col=0):\n",
    "        return df.iloc[row:row + 5, col:col + 8]\n",
    "    interact(peek, row=(0, len(df), 5), col=(0, len(df.columns) - 6))\n",
    "    print('({} rows, {} columns) total'.format(df.shape[0], df.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Qualitative Data\n",
    "\n",
    "For qualitative or categorical data, we most often use bar charts and dot charts. We will show how to create these plots using `seaborn` and the Titanic survivors dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": [
     "interactive"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c550034a97884f7fa05fd0923652bc5f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891 rows, 15 columns) total\n"
     ]
    }
   ],
   "source": [
    "# Import seaborn and apply its plotting styles\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "# Load the dataset\n",
    "ti = sns.load_dataset('titanic').reset_index(drop=True)\n",
    "\n",
    "# This table is too large to fit onto a page so we'll output sliders to\n",
    "# pan through different sections.\n",
    "df_interact(ti)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Charts\n",
    "\n",
    "In `seaborn`, there are two types of bar charts. The first type uses the `countplot` method to count up the number of times each category appears in a column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IBgDAGNEAABhzazSKi4t1zz33KDIyUuPGjdPGjRslSVVVVUpLS1NkZKRiY2OVm5vr2qe+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2078a3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Counts how many passengers survived and didn't survive and\n",
    "# draws bars with corresponding heights\n",
    "sns.countplot(x='alive', data=ti);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EMRpERCSM0SAiImGMBhERCWM0iIhIGKNB1Eo8PT3b1YnqiABGg4iIngCjQURE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\njAYREQljNIiISBijQUREwhgNIiIS9n8YX45FF4LHNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a207adcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='class', data=ti);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ju9cpIiIVIDS8vb3ZvHkzCxYsoLi4mBMnTpCYmEivXr0AiIyMJCkpiV9++YWM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\npaVNoAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119556a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# As with box plots, we can break down each category further using color\n",
    "sns.countplot(x='alive', hue='class', data=ti);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `barplot` method, on the other hand, groups the DataFrame by a categorical column and plots the height of the bars according to the average of a numerical column within each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GAEBgIQwAAANhAAAYCAMAwEAYAAAGwgAAMBAGAICBMAAADIQBAGAgDAAAA2EAABgIAwDA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\nCAMAwEAYAACG/wdZxPbU0xzzkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f513358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For each set of alive/not alive passengers, compute and plot the average age.\n",
    "sns.barplot(x='alive', y='age', data=ti);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The height of each bar can be computed by grouping the original DataFrame and averaging the `age` column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alive</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>30.626179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>28.343690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             age\n",
       "alive           \n",
       "no     30.626179\n",
       "yes    28.343690"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ti[['alive', 'age']].groupby('alive').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the `barplot` method will also compute a bootstrap 95% confidence interval for each averaged value, marked as the black lines in the bar chart above. The confidence intervals show that if the dataset contained a random sample of Titanic passengers, the difference between passenger age for those that survived and those that didn't is not statistically significant at the 5% significance level.\n",
    "\n",
    "These confidence intervals take long to generate when we have larger datasets so it is sometimes useful to turn them off:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a209093c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='alive', y='age', data=ti, ci=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dot Charts\n",
    "\n",
    "Dot charts are similar to bar charts. Instead of plotting bars, dot charts mark a single point at the end of where a bar would go. We use the `pointplot` method to make dot charts in `seaborn`. Like the `barplot` method, the `pointplot` method also automatically groups the DataFrame and computes the average of a separate numerical variable, marking 95% confidence intervals as vertical lines centered on each point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J2v5niat8dywGAxF1KX3vY6IbJ8d1LAYDEXUpoSNc29Tf0lyCQB95J1XTMzEY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2147b518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For each set of alive/not alive passengers, compute and plot the average age.\n",
    "sns.pointplot(x='alive', y='age', data=ti);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dot charts are most useful when comparing changes across categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZnpsDBgURPTSlUok9yX9h+6EcjWfJn2hrjeix3WFnY2GQ2qjhMCiI6JGdvHAb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1194abd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shows the proportion of survivors for each passenger class\n",
    "sns.pointplot(x='class', y='survived', data=ti);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GKFQqFf78cw/+/vtPCIVCtGnjB4VCodX1xOVysXLlj9i8eQOmTZsILpeLd94ZgzFjxqGo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\n3dJoY2NjAxcXF73eh2EY0HAGIYTUHaN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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1196d1d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shows the proportion of survivors for each passenger class,\n",
    "# split by whether the passenger was an adult male\n",
    "sns.pointplot(x='class', y='survived', hue='adult_male', data=ti);"
   ]
  }
 ],
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